FM Broadcast Audio Signal Analysis Using Time–Frequency Distribution Function

2020 ◽  
pp. 387-395
Author(s):  
Kartik Patel ◽  
Paawan Sharma
Keyword(s):  
Distribution Function ◽  
Frequency Distribution ◽  
Signal Analysis ◽  
Audio Signal ◽  
Time Frequency ◽  
Frequency Distribution Function ◽  
Time Frequency Distribution ◽  
Audio Signal Analysis ◽  
Fm Broadcast

Cross time-frequency distribution function

2000 ◽  
Author(s):  
YongJune Shin ◽  
Edward J. Powers ◽  
William M. Grady ◽  
S. C. Bhatt
Keyword(s):  
Distribution Function ◽  
Frequency Distribution ◽  
Time Frequency ◽  
Frequency Distribution Function ◽  
Time Frequency Distribution

Ultrasound Doppler Sinusoidal Shift Signal Analysis by Time-Frequency Distribution with New Kernel

1998 ◽  
Vol 37 (Part 1, No. 5B) ◽  
pp. 3064-3067
Author(s):  
Yasuaki Noguchi ◽  
Kohtaro Watanabe ◽  
Eiichi Kashiwagi ◽  
Takeo Hamada ◽  
Ken Mariko ◽  
...  
Keyword(s):  
Frequency Distribution ◽  
Signal Analysis ◽  
Ultrasound Doppler ◽  
Time Frequency ◽  
Time Frequency Distribution

Human Gait Signal Analysis Using Time-Frequency Distribution with Complex-Lag

2013 ◽  
Vol 411-414 ◽  
pp. 1535-1538
Author(s):  
Li Wu ◽  
Yue Yang ◽  
Lei Wang ◽  
Shu Sheng Peng
Keyword(s):  
Frequency Distribution ◽  
Signal Analysis ◽  
Gait Recognition ◽  
Processing Method ◽  
Human Gait ◽  
Public Security ◽  
Biometric Identification ◽  
Signal Model ◽  
Time Frequency ◽  
Time Frequency Distribution

As a novel biometric identification technology, human gait recognition has wide applications in public security and other fields. Aiming to solving problems of conventional Time-Frequency analysis of human gait signal, the paper presented a processing method that adopts time-frequency distribution with complex-lag. Firstly, the human walking Doppler signal model and the principle of time-frequency distribution with complex-lag were introduced. Then the signal based on the model was generated and was analyzed utilizing Spectrogram, WVD and time-frequency distribution with complex-lag. The results show that the presented method has the best performance since its high time-frequency concentration and less cross-terms disturbances.

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